What is the metric used to evaluate the performance of ranked items in model tasks?

The metric used to evaluate the performance of ranked items in model tasks is Normalized Discounted Accumulative Gain (NDCG). This metric is particularly useful in scenarios where the order of the items is significant, such as search engine results or recommendation systems. NDCG takes into account not only the relevance of the items but also their positions in the ranked list. This means that highly relevant items placed at the top of the list are given more weight than those that are ranked lower, reflecting the practical user behavior where users are more likely to engage with items that appear earlier in the results.

In contrast, other metrics like Mean Squared Error (MSE) and the F1 Score assess different types of model performance. MSE focuses on quantitative predictions by measuring the average of the squares of errors, which is not suitable for ranking tasks. The F1 Score is a measure of a model's accuracy that balances precision and recall but does not address ranking at all. Lastly, the Precision-Recall Curve visualizes the trade-off between precision and recall at various thresholds, which is useful for binary classification tasks but does not evaluate the order of ranked items. Thus, NDCG is specifically tailored for evaluating ranked outputs, making it the correct choice in this